Articles
March 20, 2024

Data Entropy - The Mission Impossible of Service & Support

The article reviews the challenges faced in accurately measuring customer experience, particularly in service and support sectors, highlighting a significant knowledge gap and the need for better insights beyond traditional metrics. It emphasizes the importance of addressing data entropy and implementing effective data orchestration, neurology, cleansing, and engineering/enriching processes to unlock the full potential of AI-driven customer service and enhance overall customer experiences.

Data Entropy - The Mission Impossible of Service & Support

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Why Customer Experience is to Squishy

I will start this off by saying ... I absolutely believe the industry was wrong about the customer experience when it came to measuring the experience as it pertains to service and support. There is absolutely something to be said about the experiences customers have with our businesses and designing them to quote be as “effortless” as possible; today – that’s farther from the truth.

In recent polling senior service/support executives have indicated they believe service/support was better 15 years ago, in fact some were quoted as saying that service was much better 20 years ago. Imagine that! Here we are in the modern age of the digital economy and service, is just ... service. It might be why the term customer experience means so much to practitioners of service and support.  But what seems to be even more crazy now is the dynamic pressure that service leaders are being put under from an acumen perspective.

More specifically the ability to not only be the people ops leader and standard efficiency expert – moreover; the pressure is really about being data masters and stewards of analytics. Now, I don’t know how many of these leaders actually think beyond the scope of I just need a chat bot – but what I do know is there is a significant knowledge gap across every industry and their service/support journey as it pertains to insights. No not CSAT, not NPS, not quality scores... and ugh not tickets and cases – real insights. Well what does that mean Marcel ?? –

Well my friend ....

Does.... Insert compelling action-oriented word for you to question your tech mirage!!!  

  • The business retains the ability to consume the data of service beyond efficiency
  • The data extend and get action by the business units which caused / created demand; or can resolve the need for contact demand
  • The actions taken today based on our analytics change our contact rate, IE we get the same amount of contact demand... nothing changing, and results haven’t moved – so what – that’s it – cant get any better... wait – let me guess you want to chat bot your way out...

Okay, now I want to be clear ... its not that service teams are not focused on resolving the above, it’s that the data they have does not contain the least amount of entropy needed to make actionable decisions, to see – measure – action – resolve – transform their customer experiences via service and support.

Understanding Entropy in the Data Landscape

Entropy, in the context of data, refers to the measure of disorder or randomness within a system. Think of it as the unseen force that determines how well-structured and valuable your data is. When entropy is low, data is well-organized and provides meaningful insights. On the other hand, high entropy signifies a lack of structure, leading to fragmented, less informative data.

Now think about it like Bob Ross vs. a 4 year old on a sugar binge painting your happy trees – think about the chaos vs. the beauty. Its just too hard for service executives and businesses alike to think about their data other than a 💩 ton of noise.

Why Data Entropy Matters in Customer Service

Now, you might wonder, why does data entropy matter in the realm of customer service and support? The key lies in the partitions of your tech stack. Each segment of your technology infrastructure provides insights, but if the entropy is high, these insights remain fractional and fail to offer a comprehensive view.

Consider this scenario: a customer interacts with your chatbot, sends an email, and makes a call. Each of these interactions is a partition in your tech stack, generating insights. However, if the data lacks coherence due to high entropy, you miss the holistic understanding needed to enhance the customer experience and moreover actually SEE THE CUSTOMER EXPERIENCE.

No platform offers the ability to quantify the customer experience, its always outputs and squishy feelings. That’s the problem! We all want to improve the experience – but we don’t use metrics like AMPx Average Minutes Per Experience, Contacts Per Resolved Experience, Agents Per Experience, all of these metrics and host of others just are not reachable ... period. Of course if the Mob is around well thats a different story...

This is precisely why businesses cannot get the most out of their data. There is no end to the number of players emerging and offering solutions, and these solutions always talk about the promise of automating service, but seriously – with data entropy at its worse since the inception of service, businesses are not positioned to utilize Ai effectively beyond basic queries – and humans still trust humans to solve complex issues. Ai is going to speed up service and dumb down agents for enterprises who are not focused on improving their data, full stop – IN OTHER WORDS – Bad Data= Bad Process – YOU CAN’T TECH YOUR WAY OUT OF BAD PROCESS my friend.

Exactly why serviceMob is positioned to make Ai real for service and support, we build the data ontology ground up with our generative Ai for our customers specific industry and business type, identify critical gaps, enrich processes, get vendors to enrich their data, to produce actionable insights to help you actually see the number of customer experiences you are having, its what analytics has promised but ... never delivered.

The Role of Data Entropy in the Age of AI

In the smorgasbord era of artificial intelligence, data is the lifeblood that fuels machine learning algorithms. However, for AI to work effectively in the realm of customer service, robust data engineering is indispensable. This involves crafting effective data ontologies that structure and define the relationships within the data.

Data entropy plays a pivotal role in this context. High entropy makes it challenging for AI systems to discern patterns, leading to suboptimal decision-making. Effective data engineering, coupled with low entropy, allows AI to glean nuanced insights, understand customer behaviors, and provide proactive, personalized support. Precisely this reason Ai is great for the basics and in some cases it can dumb down agents and make it harder to solve issues as a result of the inability to model the data from the perspective of the customer vs. the operational view by which we all currently observe data.

The Future: Data Entropy as the Catalyst for AI-Driven Customer Service

As we march towards an AI-driven future for customer service and support, addressing data entropy becomes paramount. The ability to structure and organize data cohesively will determine the success of AI applications in understanding customer needs, predicting issues, and delivering unparalleled experiences.

In essence, combating data entropy is not just a technical necessity; it's a strategic imperative. It is the linchpin that will unlock the full potential of AI in transforming customer service from a reactive to a proactive, anticipatory model.

Getting Real About Your Data

As mentioned Average Minutes Per Experience is a metric along with others not accessible anywhere else in the market folks – I have had the conversation with many leaders- it just doesn’t exist – This can only be achieved if you are above the noise, you have to sit on top of all of the data of service to be able to effectively model the journey end to end, that also means sitting on top of all those wonderful Ai solutions in the market. Only then can you get what you need out of the data – that’s improved gross margin, retained customers, higher CSAT, better NPS, less FTEs. We think a big focal point for this centers around the following aspects of data: Orchestration, Neurology, Cleansing, Engineering, and Enrichment.

Data Orchestrating: Taming the Symphony of Customer Insights

Data orchestrating is akin to conducting a symphony. It's about harmonizing the various instruments, ensuring each note aligns seamlessly. In the context of customer service, data orchestrating involves bringing together insights from every touchpoint – be it chatbots, emails, or calls. It's not just about having data; it's about making it sing in unison.

Why does this matter? Well, imagine if your chatbot is humming a tune, but your email insights are off-key. It creates a cacophony, making it impossible to discern the true melody of your customer's journey. Data orchestrating ensures that every interaction contributes to the symphony of understanding, providing a harmonized, coherent view.

Data Neurology: Understanding the Nervous System of Your Business

Data is the nervous system of your business, relaying signals from every customer touchpoint to the decision-making brain. Data neurology is about understanding this intricate network. It involves deciphering the impulses – the patterns, preferences, and pain points – that flow through your data channels.

Consider a customer navigating through your website, engaging with your chat support, and later, reaching out via email. Each of these actions sends signals, and data neurology interprets these signals to understand the holistic customer experience into quantifiable evidence of those experiences. It's about more than just response times or CSAT; it's about decoding the nuanced language of your customer's interactions and resolving experiences.

Data Cleansing: Scrubbing Away the Noise

Now, imagine your data as a canvas. Data cleansing is the meticulous process of removing the unintended splatters and chaotic strokes, leaving you with a pristine masterpiece. In customer service, data cleansing involves scrubbing away inaccuracies, redundancies, and inconsistencies.

Why does this matter? Because a clean canvas allows for a true representation of your customer's journey. If your data is cluttered with inaccuracies, it's like trying to paint a masterpiece on a canvas covered in mud. Data cleansing ensures that every brushstroke – every customer interaction – contributes to a clear and accurate portrayal.

Data Engineering / Enriching: Crafting the Building Blocks of Customer Understanding

Imagine your data as a raw material – unrefined and scattered. Data engineering is the process of crafting this raw material into well-defined building blocks. It involves structuring the data, defining relationships, and enriching it with context.

Why is this crucial? Think of it as building a house. Without a solid foundation and well-defined structure, the house collapses. Similarly, without effective data engineering, your understanding of the customer experience crumbles. It's about turning raw data into meaningful insights, creating a robust framework for understanding customer behaviors and preferences.

The Symphony of Insights: Why These Processes Matter

Now, let's tie it all together. Data orchestrating ensures that every piece of data contributes harmoniously to the overall customer symphony. Data neurology interprets the signals, providing a deep understanding of customer impulses. Data cleansing scrubs away the noise, offering a clear canvas for your customer portrait. Data engineering / enriching crafts the building blocks, creating a solid foundation for customer insights.

In the era of AI-driven customer service, these processes become the bedrock. High data entropy disrupts the symphony, making it challenging for AI systems to discern patterns and provide personalized support. Effective data orchestration, neurology, cleansing, and engineering create an environment where AI can thrive, understanding customer needs, predicting issues, and delivering unparalleled experiences.

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